Int J Neural Syst - Code-specific learning rules improve action selection by populations of spiking neurons.

Tópicos

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Resumo

Population coding is widely regarded as a key mechanism for achieving reliable behavioral decisions. We previously introduced reinforcement learning for population-based decision making by spiking neurons. Here we generalize population reinforcement learning to spike-based plasticity rules that take account of the postsynaptic neural code. We consider spike/no-spike, spike count and spike latency codes. The multi-valued and continuous-valued features in the postsynaptic code allow for a generalization of binary decision making to multi-valued decision making and continuous-valued action selection. We show that code-specific learning rules speed up learning both for the discrete classification and the continuous regression tasks. The suggested learning rules also speed up with increasing population size as opposed to standard reinforcement learning rules. Continuous action selection is further shown to explain realistic learning speeds in the Morris water maze. Finally, we introduce the concept of action perturbation as opposed to the classical weight- or node-perturbation as an exploration mechanism underlying reinforcement learning. Exploration in the action space greatly increases the speed of learning as compared to exploration in the neuron or weight space.

Resumo Limpo

popul code wide regard key mechan achiev reliabl behavior decis previous introduc reinforc learn populationbas decis make spike neuron general popul reinforc learn spikebas plastic rule take account postsynapt neural code consid spikenospik spike count spike latenc code multivalu continuousvalu featur postsynapt code allow general binari decis make multivalu decis make continuousvalu action select show codespecif learn rule speed learn discret classif continu regress task suggest learn rule also speed increas popul size oppos standard reinforc learn rule continu action select shown explain realist learn speed morri water maze final introduc concept action perturb oppos classic weight nodeperturb explor mechan under reinforc learn explor action space great increas speed learn compar explor neuron weight space

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